Comparing gws-calendar with speech
gws-calendar
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@JetBrains
Stars
56
Repository
JetBrains/skills
calendar (v3)
PREREQUISITE: Read
../gws-shared/SKILL.mdfor auth, global flags, and security rules. If missing, rungws generate-skillsto create it.
gws calendar <resource> <method> [flags]
Helper Commands
| Command | Description |
|---|---|
+insert | create a new event |
+agenda | Show upcoming events across all calendars |
API Resources
acl
delete— Deletes an access control rule.get— Returns an access control rule.insert— Creates an access control rule.list— Returns the rules in the access control list for the calendar.patch— Updates an access control rule. This method supports patch semantics.update— Updates an access control rule.watch— Watch for changes to ACL resources.
calendarList
delete— Removes a calendar from the user's calendar list.get— Returns a calendar from the user's calendar list.insert— Inserts an existing calendar into the user's calendar list.list— Returns the calendars on the user's calendar list.patch— Updates an existing calendar on the user's calendar list. This method supports patch semantics.update— Updates an existing calendar on the user's calendar list.watch— Watch for changes to CalendarList resources.
calendars
clear— Clears a primary calendar. This operation deletes all events associated with the primary calendar of an account.delete— Deletes a secondary calendar. Use calendars.clear for clearing all events on primary calendars.get— Returns metadata for a calendar.insert— Creates a secondary calendar. The authenticated user for the request is made the data owner of the new calendar.
Note: We recommend to authenticate as the intended data owner of the calendar. You can use domain-wide delegation of authority to allow applications to act on behalf of a specific user. Don't use a service account for authentication. If you use a service account for authentication, the service account is the data owner, which can lead to unexpected behavior.
patch— Updates metadata for a calendar. This method supports patch semantics.update— Updates metadata for a calendar.
channels
stop— Stop watching resources through this channel
colors
get— Returns the color definitions for calendars and events.
events
delete— Deletes an event.get— Returns an event based on its Google Calendar ID. To retrieve an event using its iCalendar ID, call the events.list method using the iCalUID parameter.import— Imports an event. This operation is used to add a private copy of an existing event to a calendar. Only events with an eventType of default may be imported. Deprecated behavior: If a non-default event is imported, its type will be changed to default and any event-type-specific properties it may have will be dropped.insert— Creates an event.instances— Returns instances of the specified recurring event.list— Returns events on the specified calendar.move— Moves an event to another calendar, i.e. changes an event's organizer. Note that only default events can be moved; birthday, focusTime, fromGmail, outOfOffice and workingLocation events cannot be moved.patch— Updates an event. This method supports patch semantics.quickAdd— Creates an event based on a simple text string.update— Updates an event.watch— Watch for changes to Events resources.
freebusy
query— Returns free/busy information for a set of calendars.
settings
get— Returns a single user setting.list— Returns all user settings for the authenticated user.watch— Watch for changes to Settings resources.
Discovering Commands
Before calling any API method, inspect it:
# Browse resources and methods
gws calendar --help
# Inspect a method's required params, types, and defaults
gws schema calendar.<resource>.<method>
Use gws schema output to build your --params and --json flags.
speech
View full →Author
@JetBrains
Stars
56
Repository
JetBrains/skills
Speech Generation Skill
Generate spoken audio for the current project (narration, product demo voiceover, IVR prompts, accessibility reads). Defaults to gpt-4o-mini-tts-2025-12-15 and built-in voices, and prefers the bundled CLI for deterministic, reproducible runs.
When to use
- Generate a single spoken clip from text
- Generate a batch of prompts (many lines, many files)
Decision tree (single vs batch)
- If the user provides multiple lines/prompts or wants many outputs -> batch
- Else -> single
Workflow
- Decide intent: single vs batch (see decision tree above).
- Collect inputs up front: exact text (verbatim), desired voice, delivery style, format, and any constraints.
- If batch: write a temporary JSONL under tmp/ (one job per line), run once, then delete the JSONL.
- Augment instructions into a short labeled spec without rewriting the input text.
- Run the bundled CLI (
scripts/text_to_speech.py) with sensible defaults (see references/cli.md). - For important clips, validate: intelligibility, pacing, pronunciation, and adherence to constraints.
- Iterate with a single targeted change (voice, speed, or instructions), then re-check.
- Save/return final outputs and note the final text + instructions + flags used.
Temp and output conventions
- Use
tmp/speech/for intermediate files (for example JSONL batches); delete when done. - Write final artifacts under
output/speech/when working in this repo. - Use
--outor--out-dirto control output paths; keep filenames stable and descriptive.
Dependencies (install if missing)
Prefer uv for dependency management.
Python packages:
uv pip install openai
If uv is unavailable:
python3 -m pip install openai
Environment
OPENAI_API_KEYmust be set for live API calls.
If the key is missing, give the user these steps:
- Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys
- Set
OPENAI_API_KEYas an environment variable in their system. - Offer to guide them through setting the environment variable for their OS/shell if needed.
- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.
If installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally.
Defaults & rules
- Use
gpt-4o-mini-tts-2025-12-15unless the user requests another model. - Default voice:
cedar. If the user wants a brighter tone, prefermarin. - Built-in voices only. Custom voices are out of scope for this skill.
instructionsare supported for GPT-4o mini TTS models, but not fortts-1ortts-1-hd.- Input length must be <= 4096 characters per request. Split longer text into chunks.
- Enforce 50 requests/minute. The CLI caps
--rpmat 50. - Require
OPENAI_API_KEYbefore any live API call. - Provide a clear disclosure to end users that the voice is AI-generated.
- Use the OpenAI Python SDK (
openaipackage) for all API calls; do not use raw HTTP. - Prefer the bundled CLI (
scripts/text_to_speech.py) over writing new one-off scripts. - Never modify
scripts/text_to_speech.py. If something is missing, ask the user before doing anything else.
Instruction augmentation
Reformat user direction into a short, labeled spec. Only make implicit details explicit; do not invent new requirements.
Quick clarification (augmentation vs invention):
- If the user says "narration for a demo", you may add implied delivery constraints (clear, steady pacing, friendly tone).
- Do not introduce a new persona, accent, or emotional style the user did not request.
Template (include only relevant lines):
Voice Affect: <overall character and texture of the voice>
Tone: <attitude, formality, warmth>
Pacing: <slow, steady, brisk>
Emotion: <key emotions to convey>
Pronunciation: <words to enunciate or emphasize>
Pauses: <where to add intentional pauses>
Emphasis: <key words or phrases to stress>
Delivery: <cadence or rhythm notes>
Augmentation rules:
- Keep it short; add only details the user already implied or provided elsewhere.
- Do not rewrite the input text.
- If any critical detail is missing and blocks success, ask a question; otherwise proceed.
Examples
Single example (narration)
Input text: "Welcome to the demo. Today we'll show how it works."
Instructions:
Voice Affect: Warm and composed.
Tone: Friendly and confident.
Pacing: Steady and moderate.
Emphasis: Stress "demo" and "show".
Batch example (IVR prompts)
{"input":"Thank you for calling. Please hold.","voice":"cedar","response_format":"mp3","out":"hold.mp3"}
{"input":"For sales, press 1. For support, press 2.","voice":"marin","instructions":"Tone: Clear and neutral. Pacing: Slow.","response_format":"wav"}
Instructioning best practices (short list)
- Structure directions as: affect -> tone -> pacing -> emotion -> pronunciation/pauses -> emphasis.
- Keep 4 to 8 short lines; avoid conflicting guidance.
- For names/acronyms, add pronunciation hints (e.g., "enunciate A-I") or supply a phonetic spelling in the text.
- For edits/iterations, repeat invariants (e.g., "keep pacing steady") to reduce drift.
- Iterate with single-change follow-ups.
More principles: references/prompting.md. Copy/paste specs: references/sample-prompts.md.
Guidance by use case
Use these modules when the request is for a specific delivery style. They provide targeted defaults and templates.
- Narration / explainer:
references/narration.md - Product demo / voiceover:
references/voiceover.md - IVR / phone prompts:
references/ivr.md - Accessibility reads:
references/accessibility.md
CLI + environment notes
- CLI commands + examples:
references/cli.md - API parameter quick reference:
references/audio-api.md - Instruction patterns + examples:
references/voice-directions.md - If network approvals / sandbox settings are getting in the way:
references/codex-network.md
Reference map
references/cli.md: how to run speech generation/batches viascripts/text_to_speech.py(commands, flags, recipes).references/audio-api.md: API parameters, limits, voice list.references/voice-directions.md: instruction patterns and examples.references/prompting.md: instruction best practices (structure, constraints, iteration patterns).references/sample-prompts.md: copy/paste instruction recipes (examples only; no extra theory).references/narration.md: templates + defaults for narration and explainers.references/voiceover.md: templates + defaults for product demo voiceovers.references/ivr.md: templates + defaults for IVR/phone prompts.references/accessibility.md: templates + defaults for accessibility reads.references/codex-network.md: environment/sandbox/network-approval troubleshooting.